Minimum classification error training of hidden Markov models for handwriting recognition

A.E. Biem
2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221)  
This paper evaluates the application of the Minimum Classification Error (MCE) training to online-handwritten text recognition based on Hidden Markov Models. We describe an allograph-based, character level MCE training aimed at minimizing the character error rate while enabling flexibility in writing style. Experiments on a writer-independent discrete character recognition task covering all alpha-numerical characters and keyboard symbols show that MCE achieves more than 30% character error rate
more » ... reduction compared to the baseline Maximum Likelihood-based system.
doi:10.1109/icassp.2001.941223 dblp:conf/icassp/Biem01 fatcat:uh7lszwr2febjpfiq3myzb3j3q